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moony211

kiwoom-private-mcp-server

by moony211
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, get_account_balance and get_accounts, have clearly distinct purposes: one retrieves detailed holdings and balance for a specific account, while the other lists all accounts with their metadata. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern (get_account_balance, get_accounts). The naming is predictable and clearly indicates the action and resource.

    Tool Count2/5

    With only two tools, the server is well below the typical 3-15 tool range for a comprehensive service. While the tools are focused, a trading platform like Kiwoom would typically require many more operations (e.g., placing orders, history), making the tool set feel incomplete.

    Completeness2/5

    The tool set only covers reading account info (list and balance). For a securities trading server, obvious operations like order management, stock search, and transaction history are missing. This leaves significant gaps that would hinder agent workflows.

  • Average 3.3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, and the description does not explicitly state that the tool is read-only or has no side effects. With no annotations, the description should include such context.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is short and front-loaded with the purpose. The 'Returns:' line is somewhat redundant given the output schema but does not detract significantly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the simplicity of the tool (zero parameters, output schema exists), the description is adequate. However, it misses the chance to contrast with the sibling tool, leaving some context incomplete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With zero parameters, schema coverage is 100%, so the baseline is 3. The description adds value by specifying that returned data includes target sectors and descriptions, going beyond the schema's lack of parameter info.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool retrieves a list of accounts with target sectors and descriptions, providing a specific verb and resource. However, it does not differentiate from the sibling 'get_account_balance'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives, lacking any when-not or context cues.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description lacks behavioral disclosures such as authentication requirements, rate limits, error handling, or side effects. With no annotations provided, the description carries the full burden, and it fails to disclose whether the operation is read-only or if it requires prior account setup.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise, front-loaded with the purpose, and structured with clear Args and Returns sections. Every sentence adds value, with no redundant or vague phrasing.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the presence of an output schema (implied by the Returns description), the description provides adequate information about the return value. However, it omits details on error handling, prerequisites (like account number validity), and the relationship to the sibling tool 'get_accounts'.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Although the schema provides no parameter descriptions (0% coverage), the description compensates by explaining 'account_no' as a Kiwoom securities account number with an example, and 'use_mock' with its default behavior based on environment settings. This adds significant meaning beyond the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb '조회' (retrieve) and clearly identifies the resource as a specific account's total evaluation assets and detailed holdings list. It distinguishes itself from the sibling tool 'get_accounts' which likely lists all accounts, thus providing clear differentiation.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    While the description explains the parameters, it does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use scenarios or preconditions are mentioned, leaving the agent to infer context from the sibling tool name alone.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Evaluate tool definition quality.

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